Google TAPAS is a BERT-Based Model to Query Tabular Data Using Natural Language

The new neural network extends BERT to interact with tabular datasets.

Jesus Rodriguez

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DataSeries

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5 min read

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Sep 7, 2020

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Querying relational data structures using natural languages has long been a dream of technologists in the space. With the recent advancements in deep learning and natural language understanding(NLU), we have seen attempts by mainstream software packages such as Tableau or Salesforce.com to incorporate natural language to interact with their datasets. However, those options remain extremely limited, constrained specific data structures and hardly resemble a natural language interaction. At the same time, we continue hitting milestones in question-answering models such as Google’s BERT or Microsoft’s Turing-NG. Could we leverage those advancements to interact with tabular data? Recently, Google Research unveiled TAPAS( Table parser), a model based on the BERT architecture that process questions and answers against tabular datasets.

Interacting with tabular data is natural language is one of those scenarios that looks conceptually trivial and results in a nightmare in the real world. Most attempts to solve this issue have been based on semantic parsing methods that process a natural language sentence and generate the corresponding SQL. That approach works in very constrained scenarios but is hardly scalable to real natural language interactions. Let’s take the following example the following datasets of American wrestling champions. The table to the right represents some possible questions that can be executed against that dataset.